Abstract:

Cognitive radio (CR) technology has emerged as
a promising solution to many wireless communication problems
including spectrum scarcity and underutilization. The knowledge
of Radio Frequency (RF) power (primary signals and/ or interfering
signals plus noise) in the channels to be exploited by CR
is of paramount importance, not just the existence or absence of
primary users. If a channel is known to be noisy, even in the
absence of primary users, using such channels will demand large
quantities of radio resources (transmission power, bandwidth, etc)
in order to deliver an acceptable quality of service to users.
Computational Intelligence (CI) techniques can be applied to
these scenarios to predict the required RF power in the available
channels to achieve optimum Quality of Service (QoS). While
most of the prediction schemes are based on the determination
of spectrum holes, those designed for power prediction use known
radio parameters such as signal to noise ratio (SNR), bandwidth,
and bit error rate. Some of these parameters may not be available
or known to cognitive users. In this paper, we developed a time
domain based optimized Artificial Neural Network (ANN) model
for the prediction of real world RF power within the GSM 900,
Very High Frequency (VHF) and Ultra High Frequency (UHF)
TV bands. The application of the models produced was found to
increase the robustness of CR applications, specifically where the
CR had no prior knowledge of the RF power related parameters.
The models used implemented a novel and innovative initial
weight optimization of the ANN’s through the use of differential
evolutionary algorithms. This was found to enhance the accuracy
and generalization of the approach